2,321 research outputs found
Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation
In the image processing pipeline of almost every digital camera there is a
part dedicated to computational color constancy i.e. to removing the influence
of illumination on the colors of the image scene. Some of the best known
illumination estimation methods are the so called statistics-based methods.
They are less accurate than the learning-based illumination estimation methods,
but they are faster and simpler to implement in embedded systems, which is one
of the reasons for their widespread usage. Although in the relevant literature
it often appears as if they require no training, this is not true because they
have parameter values that need to be fine-tuned in order to be more accurate.
In this paper it is first shown that the accuracy of statistics-based methods
reported in most papers was not obtained by means of the necessary
cross-validation, but by using the whole benchmark datasets for both training
and testing. After that the corrected results are given for the best known
benchmark datasets. Finally, the so called green stability assumption is
proposed that can be used to fine-tune the values of the parameters of the
statistics-based methods by using only non-calibrated images without known
ground-truth illumination. The obtained accuracy is practically the same as
when using calibrated training images, but the whole process is much faster.
The experimental results are presented and discussed. The source code is
available at http://www.fer.unizg.hr/ipg/resources/color_constancy/.Comment: 5 pages, 3 figure
Convolutional Mean: A Simple Convolutional Neural Network for Illuminant Estimation
We present Convolutional Mean (CM) – a simple and fast convolutional neural network for illuminant estimation. Our proposed method only requires a small neural network model (1.1K parameters) and a 48 × 32 thumbnail input image. Our unoptimized Python implementation takes 1 ms/image, which is arguably 3-3750× faster than the current leading solutions with similar accuracy. Using two public datasets, we show that our proposed light-weight method offers accuracy comparable to the current leading methods’ (which consist of thousands/millions of parameters) across several measures
High-Precision Localization Using Ground Texture
Location-aware applications play an increasingly critical role in everyday
life. However, satellite-based localization (e.g., GPS) has limited accuracy
and can be unusable in dense urban areas and indoors. We introduce an
image-based global localization system that is accurate to a few millimeters
and performs reliable localization both indoors and outside. The key idea is to
capture and index distinctive local keypoints in ground textures. This is based
on the observation that ground textures including wood, carpet, tile, concrete,
and asphalt may look random and homogeneous, but all contain cracks, scratches,
or unique arrangements of fibers. These imperfections are persistent, and can
serve as local features. Our system incorporates a downward-facing camera to
capture the fine texture of the ground, together with an image processing
pipeline that locates the captured texture patch in a compact database
constructed offline. We demonstrate the capability of our system to robustly,
accurately, and quickly locate test images on various types of outdoor and
indoor ground surfaces
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